The coronary artery disease, also known as ischemic heart disease, frequently results in a heart attack and is the most common cause of death globally. Ischemia (a restriction in blood supply to tissues) is generally caused by problems with blood vessels with resultant damage and dysfunction of tissue. Currently, the application of state-of-the-art Finite Element Methods (FEM) ischemia modelling techniques in urgent clinical situations, such as alerting patient in case of ischemia, is not possible as it requires knowledge of patient-specific anatomy which requires long measurements. We will develop a two¬step solution which alleviates these problems: 1. creation of large virtual FEM based database, 2. detection of ischemic beats and prediction of ischemia location using the developed database and data mining. The final solution will select classifiers, which are successful in detecting ischemic pulses based on potentials measured on the body surface (i.e., ECG), which will allow fast ischemia detection in urgent situations. If the ECG is classified as ischemic by the first stage classifier, it will be processed by the second stage data mining model, which will predict the location of the ischemic area.